Machines, such as construction machines (e.g., tractors, dozers, loaders, earth movers, or other such pieces of equipment), may have any number of structural components that are subject to fatigue damage which could lead to structural failures. One method for monitoring fatigue damage on a machine structure is to perform a manual, visual inspection. However, such a method may be impractical for several reasons. First, such an inspection may not be as comprehensive as desired. This may be due, in part, to the difficulty of accessing some components of the machine, such as when the structure in question is concealed and cannot be viewed without dismantling a portion of the machine. Second, a manual inspection of structural components can only be performed on a periodic basis, yet damage and resulting catastrophic failure still can occur between inspections. Third, a manual inspection may not be able to detect how much fatigue damage may have already occurred in the machine, or predict the fatigue life of one or more machine components based on the fatigue damage. While manual inspection may provide some insight into damage that is visible to an inspector, (e.g., large visible cracks in a machine component), internal damage may not be readily apparent through manual inspection (e.g., small internal cracks in a component).
Some systems have been proposed utilizing various ways of monitoring structures electronically to detect fatigue damage. However, these proposed systems have not adequately addressed the monitoring of structures with rapidly changing load pictures, such as movable machines. This is due in part to the way these proposed systems collect data about the structure. These proposed systems may collect data about the structure at a relatively low sampling rate to ease the computing burden of performing analysis on the data and storing the analysis results. However, a low sampling rate may entirely miss some load states which endure very briefly.
Many critical load states experienced by a machine may only endure very briefly. For example, when a wheel loader is digging and the bucket hits a rock, the load state may peak for a few brief moments before the rock is broken or dug out. In structures with rapidly changing load states, the sampling rate must be high in order to capture these peak load states which may endure only very briefly. If the sampling rate is too slow to capture all or most of these critical load states, the analysis results will not accurately reflect the true condition of the structure. Therefore, high sampling rates may be more appropriate for detecting the true condition of the structure, which may facilitate an accurate fatigue evaluation.
In addition, some machines have structural components that are used in harsh environments. For example, a forestry machine may be operated amongst trees and bushes with branches that can damage wires that supply power to and carry data from strain sensing devices. Therefore systems have been developed that utilize wireless strain sensing devices. However, as mentioned, high sampling rates can collect large volumes of data that can be difficult to process, and/or transmit, especially wirelessly. Wirelessly transmitting high volumes of data that result from rapid sampling may not always be practical or possible. In addition, supplying power to such wireless strain sensing devices may also present a challenge.
Systems have been developed including wireless nodes having strain sensing devices that are configured to monitor strain and process strain data at the site of measurement to reduce the volume of data to be transmitted. For example, U.S. Patent Application Publication No. 2005/0017602 to Arms et al. (“the '062 document”) discloses a system configured to monitor peak strain or strain accumulation. The system of the '062 document may include a processor at the nodes for processing data acquired by the strain sensing devices.
Although the '062 document discloses a system that may be configured to monitor various aspects of fatigue, the '062 document is not configured to predict fatigue life of a structural component using the processors at the nodes. In fact, the '062 document does not discuss predicting fatigue life at all. Predicted fatigue life can be utilized to plan maintenance schedules and preventative maintenance, such as replacement of parts. However, the system of the '062 document does not disclose making such a prediction.
The present disclosure is directed to solving one or more of the problems described above.